The GridGain in-memory computing platform includes ANSI-99 compliant SQL capabilities with DML and DDL support. You can run SQL queries across your distributed data, whether it is strictly in-memory on a data grid or it resides in a memory-centric, hybrid memory/disk database. GridGain supports free-form SQL queries with virtually no limitations and can use any SQL function, aggregation, or grouping. GridGain supports distributed SQL joins and allows for cross-cache joins, performing like an in-memory distributed SQL database. Joins between partitioned and replicated caches work without limitations while joins between partitioned data sets require that the keys are collocated. GridGain also supports the concept of fields queries to help minimize network and serialization overhead.

When GridGain is used as a complement to Apache Spark or Apache Cassandra, the SQL support in GridGain can power major performance improvements in the underlying technology:

The GridGain SQL indexing capability can improve Spark query times by 1,000x or more. Spark supports a fairly rich SQL syntax but does not support data indexing so each query requires a full data scan when used without GridGain.

GridGain enables ad hoc SQL queries on Cassandra data loaded into the GridGain using the system's in-memory distributed SQL database-like capabilities. With no SQL support, ad hoc queries are not supported by Cassandra